1. Introduction
β-1,4-N-acetylgalactosamine transferase 2 (
B4GALNT2) belongs to the family of N-acetyl galactosaminyl transferase (GalNAc-Tases), which is mainly involved in the formation of O-glycosylation [
1]. O-glycosylation is intricately associated with cellular recognition, adhesion, immune response and other vital biological processes [
2]. The
B4GALNT2 gene is currently under extensive investigation for its involvement in animal immunology [
1,
3,
4], gastrointestinal disorders [
5,
6,
7,
8,
9], and its association with litter size. Additionally,
B4GALNT2 has been identified as a potential candidate for the genetic regulation of
FecL mutations in sheep [
10]. Furthermore, the
FecL gene and its fertile allele
FecLL are among the critical genetic factors influencing ovulation in sheep [
11]. Additionally, the
B4GALNT2 gene has been implicated in congenital muscular dystrophy [
12], and its expression in the muscles of mice is dynamic [
13]. The manipulation of
B4GALNT2 expression can affect the expression of several modifiers associated with muscular dystrophy, and the deletion of this gene exacerbates the severity of congenital muscular dystrophy in mice [
14]. These findings suggest that
B4GALNT2 plays a role in muscle development.
The majority of investigations on the impact of
B4GALNT2 polymorphisms on animal production characteristics have primarily focused on sheep reproductive traits [
15,
16,
17]. While
B4GALNT2 is located on chromosome 19 in goats, studies describing
B4GALNT2 variations associated with the number of lambs in Inner Mongolia White cashmere goats have been reported [
18]. However, there is a lack of research investigating changes in goat
B4GALNT2 performance, particularly regarding growth parameters.
Boer goats are known for their excellent growth rate and meat production performance, and the 37,020,001~37,180,000 region of chromosome 19 contains many highly selective SNPs, among which the
B4GALNT2 gene and its non-coding region are located [
19]. In our previous studies [
20,
21], this gene and its adjacent regions were strongly selective among Chinese native goat breeds with high reproductive performance and small body size (Meigu goat and Jianchang Black goat), which suggested that
B4GALNT2 may be a candidate gene affecting the performance and growth traits in goats as well. Consequently, we postulated that genetic variations at the
B4GALNT2 gene loci were associated with growth traits and litter size in goats.
Through this study, we aimed to establish a theoretical basis for selecting and breeding Nanjiang Yellow goats by identifying functional regions containing regulatory elements and investigating the association between these B4GALNT2 gene variants and growth traits at different stages of development, as well as litter size.
2. Materials and Methods
2.1. Animals and Samples Collection
The Nanjiang Yellow goat population (n = 348) used in the experiment originated exclusively from the Nanjiang Yellow goat stock farm. All goats were subjected to identical management practices and environmental conditions throughout this study. Grazing and appropriate supplementary feeding were employed to raise the goats, ensuring their dietary nutrient levels met their growth requirements. In total, 1.5 mL of whole blood was collected from each test goat via jugular vein puncture, anticoagulated with heparin sodium, and stored at −20 °C for subsequent genomic DNA extraction. Pregnant ewes were randomly selected (n = 3) (unrelated) and humanely sacrificed. Different tissues (longissimus dorsi muscle (LD), lung, heart, spleen, liver, and kidney) were obtained.
2.2. Skeletal Muscle Satellite Cells (MuSCs) Isolation and Identification
According to previous methods, the LD muscle of the 1-day-old goat (male) was successfully used to isolate the MuSCs for this study [
20]. Then, we used the antibody against myogenic marker genes Pax7 (Santa Cruz, CA, USA) and MyHC (Santa Cruz, CA, USA) for immunofluorescence. We stored MuSCs in liquid nitrogen tanks. The identification results are shown in
Figure S1.
2.3. Cell Culture and Transfection
MuSCs were cultured at 5% CO
2 and 37 °C in growth medium containing 89% Dulbecco’s modified eagle medium (DMEM), 10% fetal bovine serum (FBS; Gibco, NY, USA), and 1% penicillin-streptomycin (Invitrogen, NY, USA) [
21]. Plasmids were transfected into MuSCs using Lipofectamine 3000 (Life Technologies, Carlsbad, CA, USA).
2.4. Plasmid Construction and RNA Stability Assays
The CDS sequences of the
B4GALNT2 (NM_001314262.1) gene were amplified with specific primers, and the full length of CDS was inserted into the pEGFP-N1 (Promega, WI, USA) vector using a homologous recombinant cloning kit (Vazyme, Nanjing, China) to construct overexpression plasmids. A site-directed mutagenesis kit (Vazyme, Nanjing, China) was used to obtain mutant sequences, and the vector was constructed in the same way. Primers were designed via single-fragment cloning (vazyme.com (1 May 2022)) CE Design and are listed in
Tables S5 and S6.
Actinomycin D (AcTD, A1410, Sigma-Aldrich, St. Louis, MO, USA) was used on
B4GALNT2-G or
B4GALNT2-G MuSCs for 0 h, 1 h, 2 h, 4 h, and 6 h to inhibit global mRNA transcription [
22].
2.5. Total RNA Isolation and qPCR
Total RNA was isolated from tissues and MuSCs using RNAiso Plus (Takara, Dalian, China). The cDNAs were obtained using the PrimeScript™ RT kit (Takara, Dalian, China). In addition, SYBR Premix Ex TaqTM II (Takara, Dalian, China) was used for qPCR. GAPDH was used as a reference gene and the 2
−ΔΔCt method was applied to normalize relative RNA expression. Primers are shown in
Table S3.
2.6. Luciferase Reporter Assays
The fragments containing five single-nucleotide polymorphism (SNP) sites (rs660965343, rs649127714, rs639183528, rs652899012 and rs649573228) were separately inserted into the pGL3-promoter vector (Knp I and Xho I were restriction sites). Wild-type (WT) and mutation-type (MUT) plasmids were transfected into H293T and MuSCs, respectively. The dual-luciferase reporter kit (Transgen, Beijing, China) was used to detect luciferase activity. Primers used for restricting enzyme digestion are shown in
Table S7.
2.7. Extraction of Genomic DNA and Detection of DNA Quality
Goat genomic DNA was extracted with a routine blood genome extraction kit (Tiangen, Beijing, China) and then subjected to 1.5% agarose gel electrophoresis and ultraviolet imaging in gel image analyzer BIO-RAD ChemDOC XRS. The images were analyzed using Quality One 4.6.2 software to determine DNA integrity. The purity and concentration of DNA were determined using a nucleic acid protein detector (BIO-RAD, Hercules, CA, USA). The samples that met the requirements were stored at −20 °C for later use. Gel electrophoresis is shown in
Figure S2.
2.8. PCR Amplification and Sequencing
Based on the SNP position, each SNP and its flanking sequences were retrieved from the Ensembl database, and primers were designed using the sequence as a template using Primer Premier 5.0 software and were synthesized by Sangon (Shanghai, China). The birth record table of each goat in the Nanjiang Yellow goat breeding farm was consulted, and 20 DNA samples were selected and diluted to a concentration of 20 ng/μL. From each sample, 2 μL of DNA was extracted and thoroughly mixed. The resulting mixed pool of DNA served as the template for PCR amplification. The PCR products were sent to Shanghai Sangon (Shanghai, China) for bidirectional Sanger sequencing. SnapGene6.0.2 software was used to verify the SNPS in the samples by comparing the sequencing results with the reference genome sequence and SNP sites. Primers are shown in
Table S4.
2.9. MassARRAY Genotyping
In total, 348 Nanjiang Yellow goats were genotyped using the Sequenom MassARRAY genotyping technique. According to the information of 6 SNP sites in the DNA samples of 348 Nanjiang Yellow goats, SNP sites and the information of 100 bp upstream and downstream sequences were obtained through the Ensembl database. Subsequently, we amplified the fragments containing SNP sites with the single-base primer extension method, combined with MALDI-TOF, and distinguished genotypes according to their molecular weight. The blood genomic DNA of all samples was submitted to Fuyu Biotechnology (Beijing, China) for genotyping.
2.10. Growth Trait Determination
The birth weight, body weight (BW), body length (BL), body height (BH), and chest circumference (CC) of Nanjiang Yellow goats (
n = 348) were measured using standard methods at the ages of 6 months, 12 months, and 18 months. Birth weight: weight taken within 12 h of birth; BW: body weight measured three times using the steelyard to take the average; BL: straight line distance from the leading edge of the scapula to the hip; BH: the vertical distance from the highest point of the girth to the ground; CC: the length around the chest from the back end of the shoulder blade. The primary data on the growth development and reproductive performance of Nanjiang Yellow goats are shown in
Table S9.
2.11. Bioinformatics Analysis and Data Analysis
Jaspar (
http://jaspar.genereg.net/ (accessed on 2 March 2022)) was used to predict changes in transcription factor binding at mutation sites in non-coding regions.
Haploview4.2 was used to calculate Hardy–Weinberg equilibrium and analyze linkage disequilibrium among SNPs. PHASE 2.1.1 software was used to construct haplotypes. SAS 9.4 software was used to analyze the association between genotypes of each locus and the growth traits of Nanjiang Yellow goats, and the GLM model in SAS 9.4 was used to establish the model. Yijkl = μ + Gi + Sj +Pk + Dl + eijkl, where Yijkl represented the phenotypic observations; μ was the averaged values; Gi was the fixed effect of genotype; Sj was the fixed effect of sex; Pk was the fixed effect of place; Dl was the fixed effect of date of birth (year and month); and eijkl was the random effect. All values were expressed as mean ± standard deviation. The results with p < 0.05 were considered statistically significant.
4. Discussion
B4GALNT2, highly expressed in sheep’s ovaries [
17], has received attention as a critical candidate gene affecting lamb birth and ovulation rate [
22,
23,
24]. The
B4GALNT2 protein of goats has close homology with that of sheep (
Figure S4), and it has been reported that
B4GALNT2 mRNA is expressed in the ovaries, uteri, and fallopian tubes of goats [
25]. Consistent with this, we found that the expression of
B4GALNT2 was highest in the uteri of the Nanjiang Yellow goats. These results suggest that
B4GALNT2 may also be critical for goats, but this gene has been poorly studied in goats.
The
B4GALNT2 gene and its non-coding region 37,020,001–37,180,000 on chromosome 19 of Boer goats have strong selection signals [
19], and they are also strongly selected in small Chinese local goat breeds (Meigu goats and Jianchang goats) [
26,
27]. This suggests that this gene is closely related to the growth and development of goats. However, goats’ variations in the
B4GALNT2 gene, especially those associated with growth traits, have not been extensively studied yet.
In this study, we identified six SNPs in the B4GALNT2 gene located in the conserved non-coding region and exons of the gene in 348 Nanjiang Yellow goats (known for their fast growth and high reproductive efficiency in China). We found significant associations between these SNPs and production traits and their number of lambs in Nanjiang Yellow goats, which provide insights for the further characterization of the production performance of livestock.
Synonymous mutations occurring in exons do not alter amino acid or protein sequences. However, they may regulate gene function by affecting codon bias during protein translation [
28,
29,
30], and studies have shown that synonymous mutations can affect animal production performance by influencing the efficiency of transcription and translation of genes [
31,
32,
33], for example, a synonym mutation on the IGF1 gene in Bama pigs altered the stability of
IGF1 mRNA and protein [
33]. In goats, a recent study found that GG and GA genotypes with a synonym mutation (g.37072289 G>A) in the
B4GALNT2 gene had significantly higher lamb births than other genotypes [
18]. In this study, a synonymous mutation rs672215506 (G>A) was identified in the
B4GALNT2 gene of goats. Moreover, the birth weight of the AA genotype was significantly higher than that of the GG genotype. We also found that this synonym mutation decreased the mRNA stability of
B4GALNT2 after a mutation, which might be why this site affected the birth weight of the Nanjiang Yellow goats.
SNPs in non-coding regions can indirectly regulate gene expression processes, thereby affecting animal phenotypes or reproductive performance [
34,
35]. The dual-luciferase reporter vector assay is a precise and dependable method for validating non-coding SNPs in research [
36,
37,
38]. In this study, we carried out a dual-luciferase assay in two cell types, 293T and MuSCs. Interestingly, the rs660965343 mutant-type vector showed lower luciferase activity in MuSCs. This locus may have unique effects on muscle development, but further studies are needed.
The level of genetic variation within a population is the most direct expression of genetic diversity, and the level of genetic variation can directly affect the evolutionary potential of the population [
39,
40]. Population heterozygosity is an essential indicator for judging the genetic diversity of a certain population, which can reflect the degree of genetic diversity of a population [
41]. All six SNPS in this study were in Hardy–Weinberg equilibrium, and there was rich genetic diversity within the Nanjiang Yellow goat population, which had good purification and selection potential [
42]. In addition, all loci had low (PIC ≤ 0.25) or moderate polymorphism (0.25 < PIC ≤ 0.5), with some genetic variation potential [
43,
44].
The influence of genes on traits may be influenced by the linkage effect of multiple SNPs [
45,
46]. The linkage disequilibrium of SNPs can provide more comprehensive genetic information and enhance selection efficiency [
47,
48]. In this study, the haplotype combination of H2H3 and H2H2 was found to be beneficial for increasing body weight and size, while H2H2 showed advantages for increasing the number of multiparous lambs. In addition, the mean value of the multiparous lambing number was significantly higher than the number of primiparous lambing numbers, which was in line with the results of a previous study [
49].